


import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						AutoencoderKL
    __magic_name__         =						'sample'
    __magic_name__         =						1e-2
    @property
    def 						a_     (			self       ):
         snake_case											=							4
         snake_case											=							3
         snake_case											=							(3_2, 3_2)

         snake_case											=							floats_tensor((batch_size, num_channels) + sizes       ).to(__snake_case       )

         return {"sample": image}
    @property
    def 						a_     (			self       ):
         return (3, 3_2, 3_2)
    @property
    def 						a_     (			self       ):
         return (3, 3_2, 3_2)
    def 						a_     (			self       ):
         snake_case											=							{
             '''block_out_channels''': [3_2, 6_4],
             '''in_channels''': 3,
             '''out_channels''': 3,
             '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
             '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
             '''latent_channels''': 4,
         }
         snake_case											=							self.dummy_input
         return init_dict, inputs_dict
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         pass
    @unittest.skipIf(torch_device == '''mps'''     ,   '''Gradient checkpointing skipped on MPS'''       )
    def 						a_     (			self       ):
         # enable deterministic behavior for gradient checkpointing
         snake_case					,    snake_case											=							self.prepare_init_args_and_inputs_for_common()
         snake_case											=							self.model_class(**__snake_case       )
         model.to(__snake_case       )

         assert not model.is_gradient_checkpointing and model.training

         snake_case											=							model(**__snake_case       ).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model.zero_grad()

         snake_case											=							torch.randn_like(__snake_case       )
         snake_case											=							(out - labels).mean()
         loss.backward()

         # re-instantiate the model now enabling gradient checkpointing
         snake_case											=							self.model_class(**__snake_case       )
         # clone model
         model_a.load_state_dict(model.state_dict()       )
         model_a.to(__snake_case       )
         model_a.enable_gradient_checkpointing()

         assert model_a.is_gradient_checkpointing and model_a.training

         snake_case											=							model_a(**__snake_case       ).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model_a.zero_grad()
         snake_case											=							(out_a - labels).mean()
         loss_a.backward()

         # compare the output and parameters gradients
         self.assertTrue((loss - loss_a).abs() < 1E-5       )
         snake_case											=							dict(model.named_parameters()       )
         snake_case											=							dict(model_a.named_parameters()       )
         for name, param in named_params.items():
              self.assertTrue(torch_all_close(param.grad.data     ,   named_params_a[name].grad.data     ,   atol=5E-5       )       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''     ,   output_loading_info=__snake_case       )
         self.assertIsNotNone(__snake_case       )
         self.assertEqual(len(loading_info['''missing_keys''']       )     ,   0       )

         model.to(__snake_case       )
         snake_case											=							model(**self.dummy_input       )

         assert image is not None, "Make sure output is not None"




    def 						a_     (			self       ):
         snake_case											=							AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''       )
         snake_case											=							model.to(__snake_case       )
         model.eval()

         if torch_device == "mps":
              snake_case											=							torch.manual_seed(0       )
         else:
              snake_case											=							torch.Generator(device=__snake_case       ).manual_seed(0       )

         snake_case											=							torch.randn(
             1     ,   model.config.in_channels     ,   model.config.sample_size     ,   model.config.sample_size     ,   generator=torch.manual_seed(0       )     ,   )
         snake_case											=							image.to(__snake_case       )
         with torch.no_grad():
              snake_case											=							model(__snake_case     ,   sample_posterior=__snake_case     ,   generator=__snake_case       ).sample

         snake_case											=							output[0, -1, -3:, -3:].flatten().cpu()

         # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
         # the expected output slices are not the same for CPU and GPU.
         if torch_device == "mps":
              snake_case											=							torch.tensor(
                  [
                      -4.00_78E-01,
                      -3.83_23E-04,
                      -1.26_81E-01,
                      -1.14_62E-01,
                      2.00_95E-01,
                      1.08_93E-01,
                      -8.82_47E-02,
                      -3.03_61E-01,
                      -9.86_44E-03,
                  ]       )
         elif torch_device == "cpu":
              snake_case											=							torch.tensor(
                  [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]       )
         else:
              snake_case											=							torch.tensor(
                  [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]       )

         self.assertTrue(torch_all_close(__snake_case     ,   __snake_case     ,   rtol=1E-2       )       )






@slow
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         return F'''gaussian_noise_s={seed}_shape={"_".join([str(__snake_case       ) for s in shape]       )}.npy'''
    def 						a_     (			self       ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def 						a_     (			self     ,   __snake_case=0     ,   __snake_case=(4, 3, 5_1_2, 5_1_2)     ,   __snake_case=False       ):
         snake_case											=							torch.floataa if fpaa else torch.floataa
         snake_case											=							torch.from_numpy(load_hf_numpy(self.get_file_format(__snake_case     ,   __snake_case       )       )       ).to(__snake_case       ).to(__snake_case       )
         return image
    def 						a_     (			self     ,   __snake_case="CompVis/stable-diffusion-v1-4"     ,   __snake_case=False       ):
         snake_case											=							'''fp16''' if fpaa else None
         snake_case											=							torch.floataa if fpaa else torch.floataa

         snake_case											=							AutoencoderKL.from_pretrained(
             __snake_case     ,   subfolder='''vae'''     ,   torch_dtype=__snake_case     ,   revision=__snake_case     ,   )
         model.to(__snake_case       ).eval()

         return model
    def 						a_     (			self     ,   __snake_case=0       ):
         if torch_device == "mps":
              return torch.manual_seed(__snake_case       )
         return torch.Generator(device=__snake_case       ).manual_seed(__snake_case       )
    @parameterized.expand(
        [
            # fmt: off
            [3_3, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [4_7, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model()
         snake_case											=							self.get_sd_image(__snake_case       )
         snake_case											=							self.get_generator(__snake_case       )

         with torch.no_grad():
              snake_case											=							model(__snake_case     ,   generator=__snake_case     ,   sample_posterior=__snake_case       ).sample

         assert sample.shape == image.shape

         snake_case											=							sample[-1, -2:, -2:, :2].flatten().float().cpu()
         snake_case											=							torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice       )

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=3E-3       )
    @parameterized.expand(
        [
            # fmt: off
            [3_3, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
            [4_7, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
            # fmt: on
        ]       )
    @require_torch_gpu
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model(fpaa=__snake_case       )
         snake_case											=							self.get_sd_image(__snake_case     ,   fpaa=__snake_case       )
         snake_case											=							self.get_generator(__snake_case       )

         with torch.no_grad():
              snake_case											=							model(__snake_case     ,   generator=__snake_case     ,   sample_posterior=__snake_case       ).sample

         assert sample.shape == image.shape

         snake_case											=							sample[-1, -2:, :2, -2:].flatten().float().cpu()
         snake_case											=							torch.tensor(__snake_case       )

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=1E-2       )
    @parameterized.expand(
        [
            # fmt: off
            [3_3, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [4_7, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model()
         snake_case											=							self.get_sd_image(__snake_case       )

         with torch.no_grad():
              snake_case											=							model(__snake_case       ).sample

         assert sample.shape == image.shape

         snake_case											=							sample[-1, -2:, -2:, :2].flatten().float().cpu()
         snake_case											=							torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice       )

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=3E-3       )
    @parameterized.expand(
        [
            # fmt: off
            [1_3, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
            [3_7, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
            # fmt: on
        ]       )
    @require_torch_gpu
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model()
         snake_case											=							self.get_sd_image(__snake_case     ,   shape=(3, 4, 6_4, 6_4)       )

         with torch.no_grad():
              snake_case											=							model.decode(__snake_case       ).sample

         assert list(sample.shape       ) == [3, 3, 5_1_2, 5_1_2]

         snake_case											=							sample[-1, -2:, :2, -2:].flatten().cpu()
         snake_case											=							torch.tensor(__snake_case       )

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=1E-3       )
    @parameterized.expand(
        [
            # fmt: off
            [2_7, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
            [1_6, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
            # fmt: on
        ]       )
    @require_torch_gpu
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model(fpaa=__snake_case       )
         snake_case											=							self.get_sd_image(__snake_case     ,   shape=(3, 4, 6_4, 6_4)     ,   fpaa=__snake_case       )

         with torch.no_grad():
              snake_case											=							model.decode(__snake_case       ).sample

         assert list(sample.shape       ) == [3, 3, 5_1_2, 5_1_2]

         snake_case											=							sample[-1, -2:, :2, -2:].flatten().float().cpu()
         snake_case											=							torch.tensor(__snake_case       )

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=5E-3       )
    @parameterized.expand([(1_3,), (1_6,), (2_7,)]       )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,   reason='''xformers is not required when using PyTorch 2.0.'''       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model(fpaa=__snake_case       )
         snake_case											=							self.get_sd_image(__snake_case     ,   shape=(3, 4, 6_4, 6_4)     ,   fpaa=__snake_case       )

         with torch.no_grad():
              snake_case											=							model.decode(__snake_case       ).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
              snake_case											=							model.decode(__snake_case       ).sample

         assert list(sample.shape       ) == [3, 3, 5_1_2, 5_1_2]

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=1E-1       )
    @parameterized.expand([(1_3,), (1_6,), (3_7,)]       )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,   reason='''xformers is not required when using PyTorch 2.0.'''       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model()
         snake_case											=							self.get_sd_image(__snake_case     ,   shape=(3, 4, 6_4, 6_4)       )

         with torch.no_grad():
              snake_case											=							model.decode(__snake_case       ).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
              snake_case											=							model.decode(__snake_case       ).sample

         assert list(sample.shape       ) == [3, 3, 5_1_2, 5_1_2]

         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=1E-2       )




    @parameterized.expand(
        [
            # fmt: off
            [3_3, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
            [4_7, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
            # fmt: on
        ]       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.get_sd_vae_model()
         snake_case											=							self.get_sd_image(__snake_case       )
         snake_case											=							self.get_generator(__snake_case       )

         with torch.no_grad():
              snake_case											=							model.encode(__snake_case       ).latent_dist
              snake_case											=							dist.sample(generator=__snake_case       )

         assert list(sample.shape       ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

         snake_case											=							sample[0, -1, -3:, -3:].flatten().cpu()
         snake_case											=							torch.tensor(__snake_case       )

         snake_case											=							3E-3 if torch_device != '''mps''' else 1E-2
         assert torch_all_close(__snake_case     ,   __snake_case     ,   atol=__snake_case       )








import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE     :					List[str]      =       get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE     :					Any      =       25_00_04
_SCREAMING_SNAKE_CASE     :					Any      =       25_00_20

@require_sentencepiece
@require_tokenizers
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						MBartaaTokenizer
    __magic_name__         =						MBartaaTokenizerFast
    __magic_name__         =						True
    __magic_name__         =						True
    def 						a_     (			self       ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )
         tokenizer.save_pretrained(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							'''<s>'''
         snake_case											=							0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case       )     ,   __snake_case       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0]     ,   '''<s>'''       )
         self.assertEqual(vocab_keys[1]     ,   '''<pad>'''       )
         self.assertEqual(vocab_keys[-1]     ,   '''<mask>'''       )
         self.assertEqual(len(__snake_case       )     ,   1_0_5_4       )
    def 						a_     (			self       ):
         self.assertEqual(self.get_tokenizer().vocab_size     ,   1_0_5_4       )
    def 						a_     (			self       ):
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )

         snake_case											=							tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(__snake_case     ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__snake_case       )     ,   [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,   )

         snake_case											=							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']     ,   )
         snake_case											=							tokenizer.convert_tokens_to_ids(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]     ,   )

         snake_case											=							tokenizer.convert_ids_to_tokens(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']     ,   )
    @slow
    def 						a_     (			self       ):
         # fmt: off
         snake_case											=							{'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__snake_case     ,   model_name='''facebook/mbart-large-50'''     ,   revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''     ,   )




    def 						a_     (			self       ):
         if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

         snake_case											=							(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   snake_case											=							self.rust_tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )
                   snake_case											=							self.tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )

                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )
                   snake_case											=							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f       )
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )
                        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=True
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=False
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )






@require_torch
@require_sentencepiece
@require_tokenizers
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						'facebook/mbart-large-50-one-to-many-mmt'
    __magic_name__         =						[
        ' UN Chief Says There Is No Military Solution in Syria',
        ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
    ]
    __magic_name__         =						[
        'Şeful ONU declară că nu există o soluţie militară în Siria',
        'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
        ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
        ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
    ]
    __magic_name__         =						[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
    @classmethod
    def 						a_     (			cls       ):
         snake_case											=							MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''       )
         snake_case											=							1
         return cls
    def 						a_     (			self       ):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']     ,   2_5_0_0_0_1       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']     ,   2_5_0_0_0_4       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']     ,   2_5_0_0_2_0       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']     ,   2_5_0_0_3_8       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertIn(__snake_case     ,   self.tokenizer.all_special_ids       )
         snake_case											=							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
         snake_case											=							self.tokenizer.decode(__snake_case     ,   skip_special_tokens=__snake_case       )
         snake_case											=							self.tokenizer.decode(generated_ids[1:]     ,   skip_special_tokens=__snake_case       )
         self.assertEqual(__snake_case     ,   __snake_case       )
         self.assertNotIn(self.tokenizer.eos_token     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							['''this is gunna be a long sentence ''' * 2_0]
         assert isinstance(src_text[0]     ,   __snake_case       )
         snake_case											=							1_0
         snake_case											=							self.tokenizer(__snake_case     ,   max_length=__snake_case     ,   truncation=__snake_case       ).input_ids[0]
         self.assertEqual(ids[0]     ,   __snake_case       )
         self.assertEqual(ids[-1]     ,   2       )
         self.assertEqual(len(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']       )     ,   [2_5_0_0_5_3, 2_5_0_0_0_1]       )
    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()
         snake_case											=							self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__snake_case       )
         snake_case											=							MBartaaTokenizer.from_pretrained(__snake_case       )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,   __snake_case       )
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   return_tensors='''pt'''       )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(
             self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=len(self.expected_src_tokens       )     ,   return_tensors='''pt'''     ,   )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         self.assertIsInstance(__snake_case     ,   __snake_case       )

         self.assertEqual((2, 1_4)     ,   batch.input_ids.shape       )
         self.assertEqual((2, 1_4)     ,   batch.attention_mask.shape       )
         snake_case											=							batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
         self.assertEqual(2     ,   batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens     ,   [EN_CODE]       )
         self.assertEqual(self.tokenizer.suffix_tokens     ,   [self.tokenizer.eos_token_id]       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=3     ,   return_tensors='''pt'''       )
         snake_case											=							self.tokenizer(
             text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=1_0     ,   return_tensors='''pt'''       )
         snake_case											=							targets['''input_ids''']
         snake_case											=							shift_tokens_right(__snake_case     ,   self.tokenizer.pad_token_id       )

         self.assertEqual(batch.input_ids.shape[1]     ,   3       )
         self.assertEqual(batch.decoder_input_ids.shape[1]     ,   1_0       )




    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer._build_translation_inputs(
             '''A test'''     ,   return_tensors='''pt'''     ,   src_lang='''en_XX'''     ,   tgt_lang='''ar_AR'''       )

         self.assertEqual(
             nested_simplify(__snake_case       )     ,   {
                 # en_XX, A, test, EOS
                 '''input_ids''': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 2_5_0_0_0_1,
             }     ,   )




